Influence Of Meteorological Factors Research Articles

Soil-moisture-dependent nocturnal water use strategy and its responses to meteorological factors in a seasonal-arid poplar plantation

Nocturnal water uses (Qn) significantly affect global water budgets and diurnal water cycles, which are currently suffering from restrictions of soil drought and soil water depletion caused by climate change and worldwide greening. Recent studies have recognized the vital influence of soil water availability on Qn, but responses of nocturnal water use strategy to soil moisture variation were not fully understood. For example, the effect of soil water content (θ) on the trade-offs between two components of Qn, stem water refilling (Re) and nocturnal transpiration (Tn), was rarely involved and remained unclear. This study analyzed the nocturnal sap flow of typical afforestation species, poplar plantation, under different soil water conditions in a seasonal arid region of northern China. Results indicated that higher θ significantly promoted Qn through enhancing the stomatal conductance but had less influence on the proportion of nocturnal water use to daily water use (Qn%) (7 %∼10 %). With the decline of soil water conditions, significant linear correlations between Qn and θ shifted from deep soil layers to the soil surface. The influence of meteorological factors on Qn depended on soil water condition, and the explanation of nocturnal vapor pressure deficit (VPDn) to Qn became weaker with increasing soil moisture. Negative linear relations between the proportion of stem water refilling to Qn (Re%) and θ were shown within treatments, but a significantly higher Re% was observed under more sufficient soil water. These opposite responses of Re% to θ reflected the different adaptions of Qn to soil water availability on short- and long-scales. This study highlights the crucial role of soil moisture in the nocturnal water use and balance strategy of a seasonal-arid poplar plantation. Our results help better understand the nocturnal transpiration processes in the context of global climate change.

Characteristics of Ozone Pollution and the Impacts of Related Meteorological Factors in Shanxi Province, China

Based on environmental monitoring data and meteorological observation data of the Chinese major energy province, Shanxi, from 2015 to 2020, using the satellite remote sensing data of Atmospheric Infrared Sounder Instrument (AIRS) and Ozone Monitoring Instrument (OMI) in 2017, we analyzed the characteristics of surface ozone (O3) pollution and its correlation with meteorological factors, as well as the vertical distribution of O3 in typical pollution cities in Shanxi Province. The results showed that surface O3 became the primary pollutant in Shanxi. Surface O3 has shown a zonal distribution with a high level in the south and a low level in the north region since 2017. Surface O3 pollution was severe in 2019, and the maximum daily 8 h running average of O3 (MDA8 O3) decreased, but annual mean O3 in northern and central regions showed a slow rising trend in 2020. Comprehensive analyses of the influence of meteorological factors on surface O3 indicated that O3 pollution in Linfen, Yuncheng and Taiyuan was mainly caused by local photochemical reactions, while that in Jincheng, Xinzhou, Lvliang and Yangquan resulted from regional transports. O3 volume mixing ratios (VMR) in the middle and lower troposphere generally increased with altitude, peaking at 120 ppbv at approximately 400 hPa. The positive vertical gradient of O3 in the boundary layer was obvious in Taiyuan in summer and significant in the surface layer in Taiyuan and Linfen during winter and spring, which was associated with greater atmospheric dynamic stability and suppressed vertical mixing. Due to the lack of direct detection of O3 in the lower troposphere in this region, O3 vertical distribution retrieved by satellite observation is critical for the study of vertical mixing and transport of local O3, as well as its regional transport characteristics.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the “2+26” cities of northern China

Elucidating the characteristics and influencing mechanisms of PM2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM2.5 concentrations in the “2 + 26” cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM2.5 concentrations present a U-shaped distribution. The PM2.5 concentrations in the “2 + 26” cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM2.5 concentrations in winter. The effect of anthropogenic emissions on PM2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the “2 + 26” cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Citizen science reveals meteorological determinants of frog calling at a continental scale

AbstractAimHere we investigate the strength of the relationships between meteorological factors and calling behaviour of 100 Australian frog species using continent‐wide citizen science data. First, we use this dataset to quantify the meteorological factors that best predict frog calling. Second, we investigate the strength of interactions among predictor variables. Third, we assess whether frog species cluster into distinct groups based on shared drivers of calling.LocationAustralia.MethodTo assess the relationship between calling and meteorological traits, we used spatio‐temporal subsampling (daily data fitted to 10 km2grid cells) of call and meteorological data as inputs to a boosted regression tree. We scaled the model outputs, which created a descriptive ranking of predictor importance. For strongly day‐driven species, we conducted further analyses to examine the influences of meteorological factors within the breeding season.ResultsWe found a strong seasonal signal, with day of year the strongest relationship to calling in 67 out of our 100 species, moderate relationships between temperature and calling, and weak relationships between rainfall and calling. Despite the common narratives, we found that frogs did not group into distinct categories based upon the influence of meteorological factors. For strongly day‐driven species, we found similar patterns within the breeding season.Main conclusionsWe demonstrate the importance of day of year and temperature thresholds in predicting frog calling behaviour in Australia. Understanding how meteorological conditions influence phenological events, such as breeding, will be increasingly important considering the rapid changes in environmental conditions and stability throughout most of the world, and how important breeding is to species survival.

The influence of meteorological factors on SARS-CoV-2 transmission: evidence from laboratory and epidemiological studies

SARS-CoV-2 has infected more than 600 million people worldwide and caused more than 6 million deaths. The emerging novel variants have made the epidemic rebound in many places. Meteorological factors can affect the epidemic spread by changing virus activity, transmission dynamic parameters and host susceptibility. This paper systematically analyzed the currently available laboratory and epidemiological studies on the association between the meteorological factors and COVID-19 incidence, in order to provide scientific evidence for future epidemic control and prevention, as well as developing early warning system.

Empirical analysis of the influences of meteorological factors on the interannual variations in carbon fluxes of a Quercus variabilis plantation

Influences of meteorological factors on the carbon fluxes of a forest ecosystem can vary with time scales. While many studies were made on daily to annual variations of measured carbon flux of forest ecosystems, few were conducted on the variations of observed carbon fluxes over longer (decadal or more) time scale. This study quantified the influences of meteorological variables on the measured carbon fluxes in a Quercus variabilis plantation from 2006 to 2021 using principal component analysis (PCA) and cluster analysis (CA). PCA was used to identify the most important meteorological variables. They are monthly mean daily maximum global radiation (Rg), monthly effective accumulated temperature (Tac), monthly mean daily maximum vapor pressure deficit (VPD), and monthly precipitation (PP). CA was used to group 192 observed months into four groups based on those four meteorological variables and their effects on the observed carbon fluxes. Structural equation modeling (SEM) was then used to quantify the relative importance and direct or indirect effects of those four meteorological factors on carbon fluxes at the interannual timescale in each of the four groups. The results showed that air temperature had dominant influence on net ecosystem productivity (NEP) and ecosystem respiration (Re) with its relative importance measured by standardized coefficients (SC) of 0.59 and 0.97, respectively. PP directly impacted gross primary production (GPP) with an SC of 0.40 and indirectly influenced NEP and Re. The importance of a given meteorological factor on carbon fluxes and the dominant meteorological factors varied among the four groups. Tac mainly influenced Re with a SC of 0.79 in Group 1 (the nongrowing season). The direct effects of PP on NEP and GPP and indirect effects on Re were most prominent in Group 2 (the transition stage between the nongrowing season and the growing season). Tac and PP had greater influences than other two variables in Group 3 (the transitional stage of early growth and vigorous growth and the transitional stage of steady growth and dormant period). NEP increased with Tac, while excessive rainy weather accompanied by a decrease in temperature and radiation reduced Re and GPP in Group 4 (the wet season). The short-term seasonal droughts with warm air temperatures but little precipitation (mainly in June) led to significant reductions in both GPP and NEP of the Quercus variabilis plantation ecosystem in the warm-temperate continental monsoon climate zone. Our results can be used to guide managing forest plantations in temperate region in China to maximize their carbon sequestration.

Analysis of the Influence of Meteorological Factors on Air Pollutants in Nanning from 2018 to 2020

Through the analysis of meteorological factors in Nanning from 2018 to 2020, including temperature, humidity, wind speed, sunshine, air pressure and other indicators, this study summarizes the change characteristics of meteorological factors. The descriptive statistics of the air quality in Nanning in the past three years show that PM10 and O3 are the main reasons affecting the air quality in Nanning. By means of correlation analysis and stepwise regression, the correlation degree between meteorological factors and PM10 and O3 was explored, and the relationship between independent variables and dependent variables was established. The conclusions are as follows: (1) There is a significant correlation between PM10,O3 concentrations and meteorological factors. (2) Average relative humidity, average wind speed and average air temperature can explain 42% PM 10, and average relative humidity, average wind speed, average air temperature and sunshine hours can explain 66.3% O3.

Potential effects of surface ozone on forests in Gangwon Province, South Korea, based on critical thresholds

High hourly concentrations of ozone, a secondary pollutant produced from the photochemical reactions of primary precursors, have been increasing in South Korea, bringing potential adverse effects on vegetation. Deforestation caused by high ozone concentrations has been investigated in China and Japan. Using ozone measurements from East and West, Gangwon Province, South Korea, from 2001 to 2018, this study compared changes in surface ozone concentrations and analyzed the influences of meteorological factors and air pollutants. This study calculated accumulated ozone exposure over a threshold of 40 ppb (AOT40) and investigated the possibility of ozone affecting deforestation. Monthly average surface ozone concentrations increased rapidly in both regions from 2009. Although the daily total insolation (a meteorological factor that significantly impacts photochemical reactions) of West Gangwon and East Gangwon did not differ significantly, the ozone concentration was lower in East Gangwon than in West Gangwon (1.5 times lower from 2001 to 2018) owing to local strong winds. Moreover, there was a negative correlation between nitrogen dioxide and ozone generation. AOT40 in West Gangwon was about twice that in East Gangwon and exceeded 10,000 ppbh, the critical level for forests, every year since 2003. Potential damage from high concentrations of ozone was higher in West Gangwon than in East Gangwon.

TripleConvTransformer: A deep learning vessel trajectory prediction method fusing discretized meteorological data

The shipping industry is increasingly threatened by global climate change. Reliable trajectory prediction can be used to perceive potential risks and ensure navigation efficiency. However, many existing studies have not fully considered the impact of complex ocean environmental factors and have only focused on local regions, which are difficult to extend to a global scale. To this end, we propose a deep learning vessel trajectory prediction method fusing discretized meteorological data (TripleConvTransformer). First, we clean the automatic identification system data to form a high-quality spatiotemporal trajectory dataset. Then, we fuse the trajectory data with the meteorological data after feature discretization to deeply mine the motion information of ocean-going ships. Finally, we design three modules, the global convolution, local convolution, and trend convolution modules, based on the simplified transformer model to capture multiscale features. We compare TripleConvTransformer with state-of-the-art prediction models. The experimental results show that in the prediction of the trajectory points in the next 90 min, the smallest root mean square error in terms of longitude and latitude and the highest overall prediction accuracy are achieved using TripleConvTransformer. Our method not only fully considers the influence of meteorological factors in the ocean-going process but also effectively extracts the important information hidden in the data, thus achieving accurate trajectory prediction on a global scale.

Meteorological Factors Affecting Infectious Diarrhea in Different Climate Zones of China.

Meteorological factors and the increase in extreme weather events are closely related to the incidence rate of infectious diarrhea. However, few studies have explored whether the impact of the same meteorological factors on the incidence rate of infectious diarrhea in different climate regions has changed and quantified these changes. In this study, the time series fixed-effect Poisson regression model guided by climate was used to quantify the relationships between the incidence rate of various types of infectious diarrhea and meteorological factors in different climate regions of China from 2004 to 2018, with a lag of 0–2 months. In addition, six social factors, including per capita Gross Domestic Product (GDP), population density, number of doctors per 1000 people, proportion of urbanized population, proportion of children aged 0–14 years old, and proportion of elderly over 65 years old, were included in the model for confounding control. Additionally, the intercept of each province in each model was analyzed by a meta-analysis. Four climate regions were considered in this study: tropical monsoon areas, subtropical monsoon areas, temperate areas and alpine plateau areas. The results indicate that the influence of meteorological factors and extreme weather in different climate regions on diverse infectious diarrhea types is distinct. In general, temperature was positively correlated with all infectious diarrhea cases (0.2 ≤ r ≤ 0.6, p < 0.05). After extreme rainfall, the incidence rate of dysentery in alpine plateau area in one month would be reduced by 18.7% (95% confidence interval (CI): −27.8–−9.6%). Two months after the period of extreme sunshine duration happened, the incidence of dysentery in the alpine plateau area would increase by 21.9% (95% CI: 15.4–28.4%) in that month, and the incidence rate of typhoid and paratyphoid in the temperate region would increase by 17.2% (95% CI: 15.5–18.9%) in that month. The meta-analysis showed that there is no consistency between different provinces in the same climate region. Our study indicated that meteorological factors and extreme weather in different climate areas had different effects on various types of infectious diarrhea, particularly extreme rainfall and extreme sunshine duration, which will help the government develop disease-specific and location-specific interventions, especially after the occurrence of extreme weather.

Pollution Characteristics and Source Analysis of Atmospheric PM2.5-bound Polycyclic Aromatic Hydrocarbons in a Port Area

In order to explore the pollution characteristics and potential sources of polycyclic aromatic hydrocarbons (PAHs) in the polluted air of a port area, PM2.5 samples (n=59) were collected from Qingdao Port for four seasons from August 2018 to May 2019. The seasonal variation and composition characteristics of PM2.5-bound PAHs were analyzed, the influence of meteorological factors on PAH concentrations was explored using correlation analysis, and the potential sources were analyzed using positive definite matrix factorization and potential source contribution function models. The results showed that the total mean concentration of PAHs was (8.11±12.31) ng·m-3, which was higher in autumn and winter than that in spring and summer. The seasonal molecular compositions of PAHs were similar, dominated by 4-5 ring PAHs (75.43%). Fluoranthene, benzo[e]pyrene, benzo[a]anthracene, phenanthrene, pyrene, and chrysene were the dominant species of PAHs in the study area, which are similar to the major compounds in ship exhaust. Correlation analysis showed that PAH concentrations were significantly negatively correlated with temperature and relative humidity and significantly positively correlated with atmospheric pressure and wind direction and had a poor correlation with wind speed. PMF analysis extracted six contribution factors, and the results indicated that Qingdao Port was mainly influenced by shipping emissions (28.83%), followed by vehicle emissions (20.49%), as well as crude oil volatilization (13.47%). Summer had the greatest impact on shipping emissions. The PSCF results suggested that Beijing-Tianjin-Hebei, Bohai Rim, and northern Shandong were the main source regions for long-range transport.

Influence of meteorological factors on the dynamics of hazel, alder, birch and poplar pollen in the 2021 season in Kielce, Poland.

Hazel, alder, birch, and poplar pollen allergens are a common cause of pollen allergies. In a temperate climate, wind-pollinated plants are characterized by a seasonal pollen release cycle associated with the seasons of the year and weather conditions. Therefore, the aim of the present study was to assess the course of pollen seasons of some allergenic plants and to determine the effect of meteorological factors on the content of pollen grains in the bioaerosol in 2021 in Kielce, Poland. In relation to selected meteorological parameters, the length of the total and main pollen season, the sum of daily pollen grain concentrations in the season, the peak pollen concentration, and the number of days with values exceeding the species-specific threshold concentrations, were determined. Hazel and alder pollen were the first to appear in the air of Kielce. The longest pollen season was observed for birch, while hazel was characterized by the shortest season. The alder pollen release was intense, with the highest maximum concentration of pollen grains. The study revealed a significant influence of the maximum air temperature on the dynamics of hazel, alder and poplar pollen release. Birch pollen release was significantly correlated with the average air humidity. The concentration of alder and birch pollen grains also depended on rainfall intensity. The wind force had a significant impact on the pollen season of plants. There were various relationships between the meteorological factors and the content of pollen grains in the air. The wind speed and temperature had the greatest impact on plant pollen release, with birch and alder being particularly sensitive to weather conditions.

Influence of meteorological factors on evaporative cooling of steel slag permeable concrete pavement

ABSTRACT Permeable pavement can decrease the pavement temperature through the evaporative cooling effect of water on the surface. It is necessary to understand the evaporative cooling effect of permeable pavement in the actual service environment to alleviate the urban heat island effect. In this study, a large-scale outdoor simulated evaporation test was carried out to restore the service conditions of natural soil pavement (NSP), ordinary concrete pavement (OCP) and steel slag permeable concrete pavement (SPCP) in the outdoor normal environment. Additionally, the effects of different meteorological factors (i.e. temperature, wind speed, and relative humidity) on the evaporative cooling effect of pavement were analyzed. The results inidcate that the evaporation rates of the three pavements are basically the same. Among them, the evaporation rate of NSP is the largest, followed by SPCP, and the evaporation rate of OCP is the smallest. Temperature and relative humidity have the most obvious influence on the evaporation of NSP, and wind speed has the greatest influence on the evaporation of SPCP, the three meteorological factors jointly dominate the evaporation of OCP. The scientific value of this study is to provide guidance for the subsequent prediction and evaluation of the actual cooling effect of SPCP.

Numerical simulations of the effects of green infrastructure on PM2.5 dispersion in an urban park in Bangkok, Thailand

Traffic emission has been identified as one of the dominant sources of fine particulate matter (PM2.5) in Thailand, and urban green spaces have the capacity to mitigate air pollution. Taking Bangkok as the study area, one of the most polluted cities in Thailand, this study investigated the effect of vegetation on PM2.5 concentration at three different sites with different vegetation characteristics in Chatuchak Park, an urban park located in Bangkok. Sensors were installed at the park to measure PM2.5 and metrological parameters at the roadside and different distances inside the park away from the road, and the ENVI-met model was run to simulate PM2.5 concentration in the three study sites. The result shows that PM2.5 concentration behind the vegetation barrier was reduced 34% on average compared to the concentration next to the road at the three sites. The effect of vegetation on meteorological factors was clearly seen near the park border with a hedgerow grown along the border. The order of influence of meteorological factors on PM2.5 concentration was relative humidity > potential temperature > wind speed > wind direction. Two scenarios including changes in weather patterns and types of vegetation that affect PM2.5 concentration were studied. Changing in the wind direction from oblique to perpendicular to the park had no significant effect on PM2.5 concentration as long as there is a dense hedgerow along the park border. Comparing to the current vegetation, sparse vegetation with less leaf area density and higher crown base heights had lower impact to mitigate PM2.5 concentration in the park. Our study provides information on vegetation and landscape strategies which can provide good air quality in the urban parks for better park design in the future.

Analysis of influence of meteorological factors on air pollution in Hunan Province

With the our country economy level of development and progress continuously, the serious &#x0D; destruction of ecological environment of our country, especially when the air pollution is more &#x0D; serious, so our government relevant departments in order to better improve the ecological &#x0D; environment, atmospheric environment, by adopt the method of air quality monitoring to &#x0D; understand local atmospheric pollution. In this paper, the variation trend of air quality in Hunan &#x0D; province in recent one year was analyzed and studied, including whether the concentrations of &#x0D; carbon monoxide, sulfur dioxide and nitrogen dioxide in the air were kept in the second-level &#x0D; standard, and whether the variables affecting air ozone concentration were related to the &#x0D; duration of sunshine and temperature, etc.

Simulation of Actual Evapotranspiration and Evaluation of Three Complementary Relationships in Three Parallel River Basins

Based on observed precipitation and runoff data, the monthly actual evapotranspiration (ETa) was calculated using the hydrological budget balance method in three parallel river basins. The performance of three complementary relationship methods, namely, the nonlinear advection-aridity (non-AA) method, generalized complementary relationship method (B2015), and sigmoid generalized complementary function (H2018), for simulating ETa was evaluated. The evaluation results showed that the three methods could accurately simulate monthly ETa series. The Nash–Sutcliffe efficiency coefficients between the monthly ETa simulated by the non-AA, B2015, and H2018 methods and the water balance-derived ETa were 0.74, 0.78, and 0.79, respectively. The correlation coefficients were 0.84, 0.89, and 0.90, and the root mean square errors were 10.76 mm mon−1, 10.01 mm mon−1, and 9.78 mm mon−1, respectively. ETa increased spatially from the upstream region to the downstream region at the catchment scale. Annual ETa simulated by the non-AA, B2015, and H2018 models showed significant increasing trends during the years 1956–2018 in the basins, with the increasing magnitudes of 1.53 mm/a, 1.66 mm/a, and 1.47 mm/a, respectively. Research on the influence of meteorological factors and ETa showed a positive correlation between ETa and precipitation, temperature, wind, and hours of sunshine, with average correlation coefficients of 0.40, 0.64, 0.63, and 0.72, respectively. The value between ETa and relative humidity was –0.38. ETa in the basins was highly sensitive to temperature, wind speed, and hours of sunshine, with average sensitivity coefficients of 0.26, 0.21, and 0.27, respectively. Moreover, it was moderately sensitive to relative humidity, with a sensitivity of –0.18.

On the influence of meteorological factors on population health in the conditions of the Kamchatsky Region

In the context of the intensification of the development of Kamchatka, the assessment of its comfort in terms of weather and climatic conditions is updated to prevent the influence of meteorological indicators on the health of the population. According to indicators for 2010–2020 air temperature (average and minimum), wind (average and maximum), relative humidity assessed the bioclimatic comfort of Petropavlovsk-Kamchatsky as a health risk factor. At an average temperature, the warm period of the year is June-September, there is no cold one; minimum — warm in August, cold in December-March. Average wind is strong (5 months) and strong to very strong (7 months); maximum — strong (7 months), from strong to very strong (5 months); in gusts —very strong (3 months), from very strong to stormy (6 months). The air from October to April is moderately dry, then moderately humid. With average meteorological indicators for effective temperature, the warm period of the year is in July-August (September could be both warm and transitional), cold — December-March. At the minimum temperature and maximum wind, there is no warm period of the year, cold — October-May. The influence of physical factors is inadequate for the body: uncomfortable positive temperatures in June-September, high humidity (May-September), year-round strong winds. According to the effective temperature, determined from the average values of physical factors, the warm season is shorter by 2 months, the cold 4 months. At the minimum temperature and maximum wind in the summer season, habitat conditions refer to the transition period, 8 months. to the cold season. There is no health risk based on average effective temperature data; at extreme values, the risk of cold injury within 3 months. (January, February — absolute, December — possible); with increased wind, the risk arises in the month of March.

Characterization of airborne bacteria and fungi at a land-sea transition site in Southern China

Airborne microbe can have impact on regional to global climate as ice nuclei and cloud condensation nuclei. In coastal region, microbial aerosols are simultaneously contributed by terrestrial and marine sources under the influence of land-sea air exchange. We present a study on the characteristics of airborne bacteria and fungi, including their concentrations and communities, at a land-sea transition site in Southern China from December 2019 to December 2020. Seasonal variations of microbial communities were observed with evident profiles in summer, especially for fungal aerosols. The significant enhancement of Basidiomycota abundance in summer was contributed by local biogenic release under the influence of meteorological factors. Terrestrial sources are suggested as the dominant contributors to both bacterial and fungal aerosols rather than marine sources during the whole year period. Source-tracking analysis identified that marine contributions to airborne bacteria and fungi were 3.1–14.2 % and 4–24 %, respectively. It suggests that airborne fungi should be more suitable for long-range transport than airborne bacteria. This study improves the understanding of the conversional contribution of marine and terrestrial sources to airborne microbes in coastal region and the influencing environmental factors under land-sea exchange.

Assessing the Impact of Meteorological Factors on COVID-19 Seasonality in Metropolitan Chennai, India.

Meteorological factors may influence coronavirus disease 2019 (COVID-19) transmission. Due to the small number of time series studies, the relative importance of seasonality and meteorological factors is still being debated. From March 2020 to April 2021, we evaluated the impact of meteorological factors on the transmission of COVID-19 in Chennai, India. Understanding how the COVID-19 pandemic spreads over the year is critical to developing public health strategies. Correlation models were used to examine the influence of meteorological factors on the transmission of COVID-19. The results revealed seasonal variations in the number of COVID-19-infected people. COVID-19 transmission was greatly aggravated by temperature, wind speed, nitric oxide (NO) and barometric pressure (BP) during summer seasons, whereas wind speed and BP aggravated COVID-19 transmission during rainy seasons. Furthermore, PM 2.5, NO and BP aggravated COVID-19 transmission during winter seasons. However, their relationships fluctuated seasonally. Our research shows that seasonal influences must be considered when developing effective interventions.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures.

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.